Li, Housen

Authors Cho H, Kley T, Li H Journal Journal of the Royal Statistical Society, Series B Citation J R Stat Soc Series B: Stat Method. 2025. accepted manuscript. Abstract For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed to be sparse segment-wise, which enables many

Authors Hüselitz A, Li H, Munk A Journal Arxiv Citation arXiv:2503.05270. Abstract We consider the problem of sequential estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that a certain CUSUM-type statistic attains the minimax optimal rates for localizing the change

Authors Li H, Liu Z, Munk A Journal Arxiv Citation arXiv:2503.03020. Abstract Modern large-scale data analysis increasingly faces the challenge of achieving computational efficiency as well as statistical accuracy, as classical statistically efficient methods often fall short in the first regard. In the context of testing monotonicity of a regression

Authors Requardt R, Fink M, Kubica P, Steinem C, Munk A, Li H   Journal IEEE Transactions on NanoBioScience (TNB)   Citation IEEE Transactions on NanoBioScience (TNB). 2025.   Abstract Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding

Authors Kovacs S, Li H, Haubner L, Munk A, Buhlmann P Journal Journal of Machine Learning Research Citation J Mach Learn Res 25(297):1−64, 2024. Abstract Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching one

Authors Liu Z, Li H   Journal Arxiv   Citation arXiv:2403.11356.   Abstract For robust and efficient detection of change points, we introduce a novel methodology MUSCLE (multiscale quantile segmentation controlling local error) that partitions serial data into multiple segments, each sharing a common quantile. It leverages multiple tests for

Authors Li H, Werner F   Journal Inverse Problems   Citation Inverse Problems 40 (2024) 025005 (29pp).   Abstract We consider statistical linear inverse problems in separable Hilbert spaces and filter-based reconstruction methods of the form ^fα = qα (T∗T) T∗Y, where Y is the available data, T the forward

Authors Suchan L, Li H, Munk A Journal Arxiv Citation arXiv:2308.09613. Abstract Due to its computational complexity, graph cuts for cluster detection and identification are used mostly in the form of convex relaxations. We propose to utilize the original graph cuts such as Ratio, Normalized or Cheeger Cut in order

Authors Mösching A, Li H, Munk A Journal ArXiv Citation arXiv:2305.18578. Abstract Hidden Markov models (HMMs) are characterized by an unobservable (hidden) Markov chain and an observable process, which is a noisy version of the hidden chain. Decoding the original signal (i.e., hidden chain) from the noisy observations is one

Authors Kovács S, Li H, Bühlmann P, Munk A Journal Biometrika Citation Biometrika, 2022, asac052. Abstract We propose seeded binary segmentation for large scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of